Efficient Exploration for Model-Based Reinforcement Learning

强化学习（RL）是人工智能（AI）研究的一个分支，专注于开发算法，使智能体能够在他们所处的环境中学习特定的任务，例如在游戏中最大化得分。这个学习过程的一个关键组成部分是让智能体探索能够获得最佳分数的策略。当这些代理不能有效地探索时，它们的适用性在许多用例中变得非常有限。拟议的研究将侧重于开发新的方法，使RL代理能够有效地进行勘探，使用子领域最有前途的方向：基于模型的RL。如果成功完成，这项研究将对游戏行业产生不可估量的影响，因为它将允许RL系统的可行开发，可以在许多类型的游戏中获得高水平的游戏技能，允许它们被用作具有挑战性的对手，并进行广泛的游戏测试。

Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) research that focuses on developing algorithms which enable an agent to learn a certain task in the environment they are placed in, such as maximizing the score in a game. A crucial component of this learning process is for the agent to explore strategies that result in the best possible score. When these agents are not able to explore efficiently, their applicability becomes very limited for many use cases. The proposed study will focus on developing novel approaches that allow RL agents to efficiently perform exploration, using the most promising direction of the sub-field: model-based RL. On successful completion, this study will be of invaluable impact to the gaming industry, as it will allow feasible development of RL systems that can acquire high-level playing skills in many types of games, allowing them to be used as challenging opponents, and for extensive game testing.